def compute_SVR(train_x, train_y, test_x):
# make MAE scoring
MAE = make_scorer(compute_error, greater_is_better = False)
######### SVR - Polynomial/rbf Kernel #########
# make pipeline
std_SVR = make_pipeline(StandardScaler(), SVR())
params = {'svr__kernel': ['poly', 'rbf'], 'svr__degree': [1, 2]}
gs = GridSearchCV(estimator = std_SVR, param_grid = params, scoring = MAE, n_jobs=-1, cv = 5, return_train_score = True)
# fit grid search
gs.fit(train_x, train_y)
print('SVR train score', -gs.cv_results_['mean_train_score'])
print('SVR test score', -gs.cv_results_['mean_test_score'])
print('Best Parameter', gs.best_params_)
print('Best score', -gs.best_score_)
print('Parameters', gs.cv_results_['params'])
# Train the best Model
best_SVR = make_pipeline(StandardScaler(), SVR(kernel='poly', degree=1))
best_SVR.fit(train_x, train_y)
# Make Prediction
test_y = best_SVR.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/SVR_best.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
def trainer(model,feature_selection1,train_data,test_data, data_set,split):
#features with low variance removed using formula Var(x)=p(1-p). Here, the equation is looking for features with variance below 80%
if feature_selection1:
X_all=np.concatenate((train_data[:,1:],test_data[:,1:]),axis=0)
sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
X_red=sel.fit_transform(X_all)
X_learn=X_red[:(len(X_red)/2)]
X_test=X_red[(len(X_red)/2):]
Y_learn=train_data[:,0]
X_learnS, Y_learnS=shuffle(X_learn,Y_learn)
else:
X_learnS, Y_learnS=shuffle(train_data[:,1:],train_data[:,0])
X_test=test_data[:,1:]
#best_model: trained model using optimized hyperparameters; errorF: Mean training error; c1: optimized n_estimators;
#c2: optimized max_features; c3: optimized min_samples_leaf; e1: array of training errors for n_estimators; e2: array of training errors for max_features
#e3: array of training errors for min_samples_leaf
if model=='RF':
best_model, errorF, c1, c2, c3,e1,e2,e3=cv_cv(X_learnS, Y_learnS, model, split)
print model,"for", data_set, "- Mean Training Error, optimized n_estimators,max_features,min_samples_leaf: ", errorF, c1, c2,c3
plot_line(range(1,20),e1,'n_estimators')
plot_line(np.arange(0.1,1.1,0.1),e2,'max_features')
plot_line(range(1,10),e3,'min_samples_leaf')
elif model=='DT':
best_model, errorF, c1, c2,e1,e2=cv_cv(X_learnS, Y_learnS, model, split)
print model,"for", data_set, "- Mean Training Error, optimized min_samples_leaf, max_depth: ", errorF, c1, c2
else:
best_model, errorF, c1, c2,e1,e2=cv_cv(X_learnS, Y_learnS, model, split)
print model,"for", data_set, "- Mean Training Error, optimized p, n_neighbors: ", errorF, c1, c2
reel=best_model.predict(X_test)
#
# #Save prediction file in Kaggle format
predictions = reel
kaggle.kaggleize(predictions, "../Predictions/"+data_set+"/test.csv")
def executeTrainDT(self, data, kfold, depthLst, fileTestOutputDT):
trainX = data[0]
trainY = data[1]
testX = data[2]
tree_para = {'criterion': ['gini'], 'max_depth': depthLst}
clf = GridSearchCV(DecisionTreeClassifier(),
tree_para,
cv=kfold,
n_jobs=12)
clf.fit(trainX, trainY)
meanTestAccuracy = clf.cv_results_['mean_test_score']
bestPara = clf.best_estimator_
print("DT cvResult : ", bestPara.max_depth, 1.0 - meanTestAccuracy)
kwargs = {'criterion': 'gini', 'max_depth': bestPara.max_depth}
predY = self.trainTestWholeData(trainX, trainY, testX,
DecisionTreeClassifier, kwargs)
#print ("predY DT: ", predY)
#output to file
if fileTestOutputDT != "":
kaggle.kaggleize(predY, fileTestOutputDT)
return (min(1.0 - meanTestAccuracy), kfold, bestPara.max_depth)
def indoor_localization_best():
train_x, train_y, test_x = read_data_localization_indoors()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
print('Best model for Indoor Localization Dataset')
parameter = 9
print("Training with full train data with Model: KNN for n=" +
str(parameter))
neigh = KNeighborsRegressor(n_neighbors=parameter)
neigh = neigh.fit(train_x, train_y)
y_hat = neigh.predict(train_x)
e = compute_error(y_hat, train_y)
print("MAE train error=" + str(e))
y_pred = neigh.predict(test_x)
# Create dummy test output values
predicted_y = y_pred
# Output file location
file_name = '../Predictions/IndoorLocalization/best.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
error_mat[:] = []
time_mat[:] = []
min_e = 10000000
min_n = 3
print('\n\n')
def testDataOutputFile(weights, test_x, unflatten, fileTestOutput):
(W, b, V, c) = unflatten(weights)
out = feedForward(W, b, V, c, test_x)
predY = np.argmax(out, axis=1)
#output to file
if fileTestOutput != "":
kaggle.kaggleize(predY, fileTestOutput)
def executeTrainLinearReg(self, data, kfold, alphaLst, fileTestOutputDT):
trainX = data[0]
trainY = data[1]
testX = data[2]
logReg_para = {'loss': ['hinge', 'log'], 'alpha': alphaLst}
clf = GridSearchCV(linear_model.SGDClassifier(),
logReg_para,
cv=kfold,
n_jobs=12)
clf.fit(trainX, trainY)
meanTestAccuracy = clf.cv_results_['mean_test_score']
bestPara = clf.best_estimator_
print("logistic Regreesion cvResult : ", bestPara.loss, bestPara.alpha,
1.0 - meanTestAccuracy)
kwargs = {'loss': 'hinge', 'alpha': bestPara.alpha}
predY = self.trainTestWholeData(trainX, trainY, testX,
linear_model.SGDClassifier, kwargs)
#print ("predY DT: ", predY)
#output to file
if fileTestOutputDT != "":
kaggle.kaggleize(predY, fileTestOutputDT + 'hinge')
kwargs = {'loss': 'log', 'alpha': bestPara.alpha}
predY = self.trainTestWholeData(trainX, trainY, testX,
linear_model.SGDClassifier, kwargs)
#print ("predY DT: ", predY)
#output to file
if fileTestOutputDT != "":
kaggle.kaggleize(predY, fileTestOutputDT + 'log')
return (min(1.0 - meanTestAccuracy), kfold, bestPara.alpha)
def distance_effect(train_x, train_y, test_x):
regr = KNeighborsRegressor(n_neighbors=3, p=1)
# Fit Model
regr.fit(train_x, train_y)
# Make Prediction
test_y = regr.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/KNN_Manhattan.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
regr = KNeighborsRegressor(n_neighbors=3, metric='chebyshev')
# Fit Model
regr.fit(train_x, train_y)
# Make Prediction
test_y = regr.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/KNN_chebyshev.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
def power_plant_best():
train_x, train_y, test_x = read_data_power_plant()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
print('Best model for Power Output Dataset')
parameter = 13
print("Training with full train data: Model=Decision Tree for depth=" +
str(parameter))
clf = tree.DecisionTreeRegressor(criterion='mse', max_depth=parameter)
clf = clf.fit(train_x, train_y)
y_hat = clf.predict(train_x)
e = compute_error(y_hat, train_y)
print("MAE train error=" + str(e))
y_pred = clf.predict(test_x)
# Create dummy test output values
predicted_y = y_pred
# Output file location
file_name = '../Predictions/PowerOutput/best.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
error_mat[:] = []
time_mat[:] = []
min_e = 10000000
depth_for_min_e = 3
print('\n\n')
def trainSVMExtra(fileTestOutput, resultFile):
'''
for extra credit 1
try different kernel ridge model or even different kernel
how to select effective kernel
'''
train_x, train_y, test_x = read_tumor_data()
print('Train=', train_x.shape, type(train_x))
print('Test=', test_x.shape)
#[1, 0.01, 0.001, 0.0001]
fd = open(resultFile, 'a')
kfoldLst = range(3, 12)
biggestAccuracy = -2**32
for kfold in kfoldLst:
parameters = {
'kernel': ('linear', 'rbf', 'sigmoid', 'poly'),
'C': np.linspace(1, 5, 5),
'gamma': [0.001, 0.1, 20],
'degree': np.linspace(1, 5, 5)
}
clf = GridSearchCV(SVC(), parameters, cv=kfold,
n_jobs=10) #scoring= "neg_mean_squared_error" )
clf.fit(train_x, train_y)
meanTestError = clf.cv_results_['mean_test_score']
bestPara = clf.best_estimator_
if clf.best_score_ > biggestAccuracy:
biggestAccuracy = clf.best_score_
paramtersBest = [
bestPara.C, bestPara.gamma, bestPara.degree, bestPara.kernel,
kfold, clf.best_score_
]
#print ("trainKernelRidgeExtra Result : ", bestPara.C, bestPara.gamma, bestPara.degree, bestPara.kernel, clf.best_score_, meanTestError,)
writeToFile(fd, [
bestPara.C, bestPara.gamma, bestPara.degree, bestPara.kernel,
kfold, clf.best_score_
] + list([meanTestError]))
# kwargs = {'n_neighbors': bestPara.n_neighbors}
clf = SVC(C=bestPara.C,
gamma=bestPara.gamma,
degree=bestPara.degree,
kernel=bestPara.kernel)
clf.fit(train_x, train_y)
predY = clf.predict(test_x)
#print ("predY DT: ", predY)
#output to file
if fileTestOutput != "":
kaggle.kaggleize(predY, fileTestOutput + str(kfold), False)
print("best final trainKernelRidgeExtra Result: ", paramtersBest)
def stratifyDataTrainTest3layerNN():
data = read_image_data()
train_x = data[0]
train_y_integers = data[1]
test_x = data[2]
#normalize
scaler = StandardScaler()
train_x = scaler.fit_transform(train_x)
test_x = scaler.fit_transform(test_x)
print("train_x. shape:", train_x.shape)
#split
xsplitTrain, xsplitTest, ysplitTrain_integer, ysplitTest_integer = train_test_split(
train_x,
train_y_integers,
test_size=0.2,
random_state=0,
stratify=train_y_integers)
hidden_layer_sizes_lst = [(5, 5, 5), (10, 10, 10), (40, 40, 40),
(70, 70, 70), (100, 100, 100)]
largestAccuracy = -2**32
best_hidden_layer_size = None
for hidden_layer_sizes in hidden_layer_sizes_lst:
beginTime = time.time()
mlp = MLPClassifier(hidden_layer_sizes,
activation='tanh',
max_iter=1000,
momentum=0.9,
epsilon=1e-8)
mlp.fit(xsplitTrain, ysplitTrain_integer)
#pred = mlp.predict(xsplitTest) #predict validation set
meanAccuracy = mlp.score(xsplitTest, ysplitTest_integer)
if meanAccuracy > largestAccuracy:
largestAccuracy = meanAccuracy
best_hidden_layer_size = hidden_layer_sizes
print("SstratifyDataTrainTest3layerNN. smalleAccuracy:",
time.time() - beginTime, meanAccuracy)
print("SstratifyDataTrainTest3layerNN. smalleAccuracy:", largestAccuracy,
best_hidden_layer_size)
#train and test the whole data
mlp = MLPClassifier(best_hidden_layer_size,
activation='tanh',
max_iter=1000,
momentum=0.9,
epsilon=1e-8)
mlp.fit(train_x, train_y_integers)
predyTest = mlp.predict(test_x)
#output to file
fileTestOutput3LayerNN = "../Predictions/best_3HiddenNN.csv"
if fileTestOutput3LayerNN != "":
kaggle.kaggleize(predyTest, fileTestOutput3LayerNN)
def kFold(features_train, labels_train, features_test, labels_test, path,
DataSet):
n = features_train.shape
k = 10 # make it 10 fold test
size = n[0] / k # size of each fold.
errors1 = {} # to save CrossValidation Errors
errors2 = {} # to save Training Errors
e1 = [] # to obtain mean square error of whole CrossValidation set
e2 = [] #to obtain mean square error of whole Training set
for p in range(1, 20): # considering neighbours from range 1 to 20.
print "Considering Neighbor=" + str(p)
for i in range(1, k):
# Select the cross Validation set, it will change with changing values of i
Feature_CrossVal = features_train[i * size:][:size]
Label_CrossVal = labels_train[i * size:][:size]
# Add the rest of Training set
Feature_Train = features_train[:i * size]
np.append(Feature_Train, features_train[(i + 1) * size:])
Label_Train = labels_train[:i * size]
np.append(Label_Train, labels_train[(i + 1) * size:])
# K Nearest Neighbor training with testset and testing with Cross Validation set
neigh = KNeighborsClassifier(n_neighbors=p)
neigh.fit(Feature_Train, Label_Train)
predict = np.zeros(Label_CrossVal.shape)
predict = neigh.predict(Feature_CrossVal)
# K Nearest Neighbor training and testing on same set
ne = KNeighborsClassifier(n_neighbors=p)
ne.fit(Feature_Train, Label_Train)
pre = np.zeros(Label_Train.shape)
pre = ne.predict(Feature_Train)
# Find the mean square error between the found predictions to the cross validation output set add error in a list
e1.append(mean_squared_error(Label_CrossVal, predict))
e2.append(mean_squared_error(Label_Train, pre))
print e1, e2 # printing so that it doesn't look like program got hanged :P
# take mean of the K fold errors for particular neighbor and append it
errors1[p] = np.mean(e1)
errors2[p] = np.mean(e2)
#Find predictions with Min Error Neighbor
neigh = KNeighborsClassifier(n_neighbors=min(errors1, key=errors1.get))
#fitting the training data
neigh.fit(features_train, labels_train)
predict = np.zeros(labels_test.shape)
#testing with test features
predict = neigh.predict(features_test)
kaggle.kaggleize(
predict,
str(path) + "/Submission/Predictions/" + str(DataSet) +
"/CrossValidation_KNN.csv")
#returning errors to run_me.py file for plotting CrossValidation and Training Error
return errors1, errors2
def kernelRidgeSkLearnCV(kfold=8, fileTestOutput="best_cv"):
'''
call kernel ridge functions for different parameters for credit card activity data
use cross validation to get out of sample out of sample meansquared error
'''
train_x, train_y, test_x = read_creditcard_data()
print('Train=', train_x.shape, type(train_x))
print('Test=', test_x.shape)
#train_x = normalize(train_x, axis=0)
#test_x = normalize(test_x, axis=0)
#train_x = StandardScaler().fit_transform(train_x)
#test_x = StandardScaler().fit_transform(test_x)
alphaParaLst = [1, 0.0001]
gammaParaLst = [None, 1, 0.001]
kernelParaLst = ["rbf", "polynomial", "linear"]
mseErrorSmallest = 2**32
#mseErrorLst = []
for alpha in alphaParaLst:
for gamma in gammaParaLst:
for kernel in kernelParaLst:
clf = KernelRidge(alpha=alpha,
gamma=gamma,
degree=3,
kernel=kernel)
mseError = -1 * np.mean(
cross_val_score(clf,
train_x,
train_y,
cv=kfold,
scoring="neg_mean_squared_error"))
print("mseError: ", alpha, gamma, kernel, mseError)
#mseErrorLst.append(mseError)
if mseError < mseErrorSmallest:
mseErrorSmallest = mseError
paramtersBest = [alpha, gamma, kernel]
print("best mseError: ", kfold, paramtersBest, mseErrorSmallest)
#train whole data
clf = KernelRidge(alpha=paramtersBest[0],
gamma=paramtersBest[1],
degree=3,
kernel=paramtersBest[2])
clf.fit(train_x, train_y)
yPred = clf.predict(test_x)
#output file
if fileTestOutput != "":
kaggle.kaggleize(yPred, fileTestOutput, True)
def compute_KNN(train_x, train_y, test_x):
# Different number of neighbors to run
neighbor = [3, 5, 10, 20, 25]
# Initialize Variables
train_score = [0] * len(neighbor)
test_score = [0] * len(neighbor)
fit_time = [0] * len(neighbor)
score_time = [0] * len(neighbor)
# make MAE scoring
MAE = make_scorer(compute_error, greater_is_better=False)
# indexing
index = 0
for n in neighbor:
# Create the model
regr = KNeighborsRegressor(n_neighbors=n)
# Cross Validation
cv_score = cross_validate(regr,
train_x,
train_y,
return_train_score=True,
scoring=MAE,
cv=2)
# Extract Statistics, scorer negates compute_error output
train_score[index] = -cv_score['train_score'].mean()
test_score[index] = -cv_score['test_score'].mean()
# Print Statistics
print('Number of Neighbors:', n)
print('train score', train_score[index])
print('test score', test_score[index])
print('===============================')
# Fit Model
regr.fit(train_x, train_y)
# Make Prediction
test_y = regr.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/KNearestN_Neighbors_%d.csv' % n
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
# Increase indexing
index = index + 1
def predictNextBallBayesInference(file_name):
iterations = np.arange(10000, 1000000, 10000) #[100000] #np.arange(10000, 1000000, 10000)
for iters in iterations:
prediction_prob = list()
lengths = [10, 15, 20, 25]
for l in lengths:
BArray = np.loadtxt('../../Data/B_sequences_%s.txt' % (l), delimiter=',', dtype=float)
for b in np.arange(BArray.shape[0]):
prob = getNextBallMCMCQuestionl(BArray[b, :], iters)
prediction_prob.append(prob)
#print('Prob of next entry in ', BArray[b, :], 'is black is', prediction_prob[-1])
#print('Prob of next entry in is black is', prediction_prob[-1])
print('Writing output to ', file_name + "_iteration_" + str(iters))
kaggle.kaggleize(np.array(prediction_prob), file_name + "iteration_" + str(iters))
def svmSklearnCV(kfold=7, fileTestOutput="best_cv"):
'''
call svm train and predict for different parameters for tumor data
use cross validation to get out of sample out of sample meansquared error
'''
train_x, train_y, test_x = read_tumor_data()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
cLst = [1, 0.01, 0.0001]
gammaLst = [1, 0.01, 0.001]
kernelParaLst = ['rbf', 'poly=3', 'poly=5', 'linear']
degree = 3
accuracyLargest = -2**32
for c in cLst:
for gamma in gammaLst:
for kernel in kernelParaLst:
if kernel.split("=")[0] == "poly":
degree = int(kernel.split("=")[1])
kernel = kernel.split("=")[0]
clf = SVC(C=c, kernel=kernel, degree=degree, gamma=gamma)
accuracy = np.mean(
cross_val_score(clf,
train_x,
train_y,
cv=kfold,
scoring="accuracy"))
print("accuracy: ", c, gamma, kernel, degree, accuracy)
if accuracy > accuracyLargest:
accuracyLargest = accuracy
paramtersBest = [c, gamma, degree, kernel]
print("best accuracy parameters: ", kfold, paramtersBest, accuracyLargest)
#train whole data
clf = SVC(C=paramtersBest[0],
gamma=paramtersBest[1],
degree=paramtersBest[2],
kernel=paramtersBest[3])
clf.fit(train_x, train_y)
yPred = clf.predict(test_x)
#output file
if fileTestOutput != "":
kaggle.kaggleize(yPred, fileTestOutput, False)
def power_plant_LinearModel(k_fold):
train_x, train_y, test_x = read_data_power_plant()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
alphas = [pow(10, -6), pow(10, -4), pow(10, -2), 1, 10]
y_pred = choose_best_LinearModel(train_x, train_y, test_x, alphas, k_fold)
#####plot
avg_time = []
log_alphas = []
for i in time_mat:
avg_time.append(i[k_fold])
for i in alphas:
log_alphas.append(math.log10(i))
plt.plot(log_alphas, avg_time[0:len(alphas)], 'ro', label='Lasso')
plt.plot(log_alphas,
avg_time[len(alphas):2 * len(alphas)],
'bo',
label='Ridge')
plt.legend(loc='upper center')
plt.ylabel('Avg Time for validation')
plt.xlabel('Alpha for Linear Model (Log)')
plt.title('Power Plant dataset (Model=Linear Model)')
plt.show()
predicted_y = y_pred
file_name = '../Predictions/PowerOutput/power_plant_LM.csv'
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
file2 = open('PPlogfile_LM.txt', 'w')
file2.write(str(error_mat) + '\n\n\n')
file2.write(str(time_mat))
file2.close()
#clear global variables
error_mat[:] = []
time_mat[:] = []
alpha_for_min_e = 3
flag = True
min_e = 100000000
avg_time[:] = []
log_alphas[:] = []
print('\n\n')
def compute_NN(train_x, train_y, test_x):
# make MAE scoring
MAE = make_scorer(compute_error, greater_is_better=False)
######### Neural Network #########
# make pipeline
std_NN = make_pipeline(StandardScaler(), MLPRegressor())
params = {
'mlpregressor__hidden_layer_sizes': [(10, ), (20, ), (30, ), (40, )],
'mlpregressor__max_iter': [1000]
}
gs = GridSearchCV(estimator=std_NN,
param_grid=params,
scoring=MAE,
n_jobs=-1,
cv=5,
return_train_score=True)
# fit grid search
gs.fit(train_x, train_y)
print('NN train score', -gs.cv_results_['mean_train_score'])
print('NN test score', -gs.cv_results_['mean_test_score'])
print('Best Parameter', gs.best_params_)
print('Best score', -gs.best_score_)
print('Parameters', gs.cv_results_['params'])
# Train the best Model
best_NN = make_pipeline(StandardScaler(),
MLPRegressor(hidden_layer_sizes=(20, )))
best_NN.fit(train_x, train_y)
# Make Prediction
test_y = best_NN.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/NN_best.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
def classifierKnn(train1, label1, train2, label2):
metric = 'euclidean'
k = 2
clf = neighbors.KNeighborsClassifier(n_neighbors=k,
weights='distance',
metric=metric)
clf.fit(features_train, labels_train)
#Compute a prediction for every point in the grid
for i in range(len(train2)):
x = features_test[i, :].reshape(1, -1)
predictions = clf.predict(x)
labels_test[i] = predictions
#Save prediction file in Kaggle format
#predictions = np.zeros(labels_test.shape)
for i in range(len(labels_test)):
print features_test[i, :], labels_test[ssi]
kaggle.kaggleize(labels_test, "../Predictions/Digits/test.csv")
def pipe_line_final(path, param_grid, nfs, data_set):
if data_set=='Blog':
train_m = np.load(path + 'train.npy')
X_all=train_m[:,0:train_m.shape[1]-1]
Y_all=train_m[:,-1]
f=SelectKBest(f_regression)
X_new=f.fit_transform(X_all, Y_all)
train_x, train_y=shuffle(X_new, Y_all)
selected_params=f.get_support(indices=True)
reg=GridSearchCV(GradientBoostingRegressor(), param_grid, scoring='neg_mean_squared_error')
reg.fit(train_x,train_y)
print "For ", data_set, "- best params: ", reg.best_params_, "Feature_subset: ", f.get_support(indices=True)
test=np.load(path + 'test_distribute.npy')
test_x=test[:, 0:test.shape[1]-1]
tr=[]
for i in range(0, 280):
if i in selected_params:
tr.append(True)
else: tr.append(False)
tr=np.array(tr)
X_masked=test_x[:,tr]
predictions=reg.predict(X_masked)
kaggle.kaggleize(predictions, "../Predictions/BlogFeedback/test.csv")
else:
train = np.load(path + 'train.npy')
test = np.load(path + 'test_private.npy')
train_x, train_y=shuffle(train[:, 0:train.shape[1]-1], train[:, -1])
test_x = test[:, 0:test.shape[1]-1]
test_y = test[:, -1]
rfe=RFE(DecisionTreeRegressor(), n_features_to_select=nfs, step=2)
reg=GridSearchCV(rfe, param_grid)
reg.fit(train_x,train_y)
print "For ", data_set, "- best params: ", reg.best_params_, "Feature_subset: ", reg.best_estimator_.ranking_, "RMSE: ", np.sqrt(mean_squared_error(test_y,reg.predict(test_x)))
def Experiment_DataSet(features_train, labels_train, features_test,
labels_test, path, i):
#Support Vector Regression
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
y_rbf = svr_rbf.fit(features_train, labels_train).predict(features_test)
kaggle.kaggleize(
y_rbf, path + "Submission/Predictions/" + str(i) + "/RFE_SVR.csv")
print "Support Vector Regression"
#K Nearest Neighbor
neigh = KNeighborsRegressor(n_neighbors=6)
neigh.fit(features_train, labels_train)
predict = neigh.predict(features_test)
kaggle.kaggleize(
predict, path + "Submission/Predictions/" + str(i) + "/KNN_REG.csv")
print "K Nearest Neighbor"
#LassoCV with RFE and pipeline merge of both
alpha = np.arange(0.1, 2, 0.1) #defining range of alphas
lasso = linear_model.LassoCV(alphas=alpha)
rfe = RFE(estimator=lasso, step=1)
Lasso_Pipeline = make_pipeline(rfe, svr_rbf)
Lasso_Pipeline.fit(features_train, labels_train)
predict = Lasso_Pipeline.predict(features_test)
kaggle.kaggleize(
predict, path + "Submission/Predictions/" + str(i) + "/LassoCV.csv")
print "LassoCV with RFE and pipeline"
def final_model(train_x, train_y, test_x):
np.random.seed(2018)
# make MAE scoring
MAE = make_scorer(compute_error, greater_is_better=False)
scaler = StandardScaler()
scaler.fit(train_x)
train_x = scaler.transform(train_x)
test_x = scaler.transform(test_x)
# create model
model = Sequential()
model.add(Dense(20, input_shape=(52, ), activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(20, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(20, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(1, activation='relu'))
opti_adam = optimizers.Adam(lr=0.1, beta_1=0.9)
# Compile model
model.compile(loss='MAE', optimizer=opti_adam, metrics=['accuracy'])
# Fit the model
model.fit(train_x, train_y, epochs=150, batch_size=200)
# evaluate the model
scores = model.evaluate(train_x, train_y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
test_y = model.predict(test_x)
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/NN_best_competition.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
def pipe_line(path, test_y_p, param_grid, nfs, data_set):
if test_y_p:
train = np.load(path + 'train.npy')
test = np.load(path + 'test_private.npy')
else:
train_m = np.load(path + 'train.npy')
train=train_m[:(len(train_m)/2)]
test = train_m[(len(train_m)/2):]
train_x, train_y=shuffle(train[:, 0:train.shape[1]-1], train[:, -1])
test_x = test[:, 0:test.shape[1]-1]
test_y = test[:, -1]
rfe=RFE(DecisionTreeRegressor(), n_features_to_select=nfs, step=2)
reg=GridSearchCV(rfe, param_grid)
reg.fit(train_x,train_y)
print "For ", data_set, "- best params: ", reg.best_params_, "Feature_subset: ", reg.best_estimator_.ranking_, "RMSE: ", np.sqrt(mean_squared_error(test_y,reg.predict(test_x)))
if not test_y_p:
test_f=np.load(path + 'test_distribute.npy')
predictions=reg.predict(test_f[:, 0:test.shape[1]-1])
kaggle.kaggleize(predictions, "../Predictions/BlogFeedback/test.csv")
def indoor_localization_KNN():
train_x, train_y, test_x = read_data_localization_indoors()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
k_fold = 5
neighbour_list = [3, 5, 10, 20, 25]
y_pred = choose_best_KNN(train_x, train_y, test_x, k_fold, neighbour_list)
# Create dummy test output values
predicted_y = y_pred
# Output file location
file_name = '../Predictions/IndoorLocalization/indoor_localization_KNN.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
file2 = open('ILlogfile_KNN.txt', 'w')
file2.write(str(error_mat) + '\n')
file2.write(str(time_mat))
file2.close()
#######plot
avg_time = []
for i in time_mat:
avg_time.append(i[k_fold])
graph = plt.plot(neighbour_list, avg_time, 'rs')
plt.ylabel('Avg Time for Validation(ms)')
plt.xlabel('NUmber of neighbours')
plt.title('Indoor Localization dataset (Model=KNN)')
plt.show()
error_mat[:] = []
time_mat[:] = []
min_e = 10000000
min_n = 3
avg_time[:] = []
print('\n\n')
def power_plant_DT(k_fold):
train_x, train_y, test_x = read_data_power_plant()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
depths = [3, 6, 9, 12, 15]
y_pred = choose_best_DT(train_x, train_y, test_x, depths, k_fold)
#######plot
avg_time = []
for i in time_mat:
avg_time.append(i[k_fold])
graph = plt.plot(depths, avg_time, 'rs')
plt.ylabel('Avg Time for Validation(ms)')
plt.xlabel('Depth of Tree')
plt.title('Power plant dataset (Model=Decision Tree)')
plt.show()
######writing predictions to CSV file
predicted_y = y_pred
file_name = '../Predictions/PowerOutput/power_plant_DT.csv'
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
#######logs
file2 = open('PPlogfile_DT.txt', 'w')
file2.write(str(error_mat) + '\n\n')
file2.write(str(time_mat) + '\n\n')
file2.write(str(avg_time) + '\n')
file2.close()
error_mat[:] = []
time_mat[:] = []
avg_time[:] = []
min_e = 10000000
depth_for_min_e = 3
print('\n\n')
def indoor_localization_DT(k_fold):
train_x, train_y, test_x = read_data_localization_indoors()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
depths = [3, 6, 9, 12, 15]
y_pred = choose_best_DT(train_x, train_y, test_x, depths, k_fold)
#####plot avg time
avg_time = []
for i in time_mat:
avg_time.append(i[k_fold])
plt.plot(depths, avg_time, 'bo')
plt.ylabel('Avg Time for Validation')
plt.xlabel('Depth of Tree')
plt.title('Indoor Localisation Dataset')
plt.show()
#####write predictions in CSV file
predicted_y = y_pred
file_name = '../Predictions/IndoorLocalization/indoor_localization_DT.csv'
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
#####log file
file2 = open('ILlogfile_DT.txt', 'w')
file2.write(str(error_mat) + '\n\n')
file2.write(str(time_mat) + '\n\n')
file2.write(str(avg_time) + '\n')
file2.close()
error_mat[:] = []
time_mat[:] = []
avg_time[:] = []
min_e = 10000000
depth_for_min_e = 3
print('\n\n')
def executeTrainKNN(self, data, kfold, knnLst, fileTestOutputDT):
trainX = data[
0] #[0:1000, : ] #smaller first for debugging
trainY = data[1] #[0:1000]
testX = data[2]
knn_para = {'n_neighbors': knnLst}
clf = GridSearchCV(KNeighborsClassifier(),
knn_para,
cv=kfold,
n_jobs=12)
clf.fit(trainX, trainY)
meanTestAccuracy = clf.cv_results_['mean_test_score']
bestPara = clf.best_estimator_
print("KNN cvResult : ", bestPara.n_neighbors, 1.0 - meanTestAccuracy)
kwargs = {'n_neighbors': bestPara.n_neighbors}
predY = self.trainTestWholeData(trainX, trainY, testX,
KNeighborsClassifier, kwargs)
#print ("predY DT: ", predY)
#output to file
if fileTestOutputDT != "":
kaggle.kaggleize(predY, fileTestOutputDT)
def CrossValidation_Robot(features_train,labels_train,features_test,labels_test,path):
n=features_train.shape
k=2 # make it 2 fold
size = n[0]/k # size of each fold.
errors1={}
e1=[]
errors2={}
Cvalue=np.arange(100,500,100)
degree=np.arange(1,n[1],2)
for d in degree:
for p in Cvalue: # considering neighbours from range 1 to 20.
print "Considering"+str(p)
for i in range(1,k):
# Select the cross Validation set, it will change with changing values of i
Feature_CrossVal = features_train[i*size:][:size]
Label_CrossVal = labels_train[i*size:][:size]
# Add the rest of Training set
Feature_Train = features_train[:i*size]
np.append(Feature_Train ,features_train[(i+1)*size:])
Label_Train = labels_train[:i*size]
np.append(Label_Train ,labels_train[(i+1)*size:])
#define Support Vector Regression with Degree and Regression Coefficient as Hyperparameter
svr_rbf = SVR(kernel='rbf', C=p, degree=d)
y_rbf = svr_rbf.fit(Feature_Train, Label_Train).predict(Feature_CrossVal)
# Find the mean square error between the found predictions to the cross validation output set add error in a list
e1.append(np.sqrt(mean_squared_error(Label_CrossVal,y_rbf)))
print e1
# take mean of the K fold errors for particular neighbor and append it
errors1[p]=np.mean(e1) #saving absolute error with Value of regularization constant
errors2[d]=np.mean(e1) #saving absolute error with Value of Polynomial Degree
#Find predictions with Min Error degree and Regression Coefficient
print min(errors1, key=errors1.get)
#define the Support Vector Regression with optimum Regression Coefficient and Degree
svr_rbf = SVR(kernel='rbf', C=min(errors1, key=errors1.get), degree=min(errors1, key=errors2.get))
predict=np.zeros(labels_test.shape)
#fit the data and predict the values
predict = svr_rbf.fit(features_train, labels_train).predict(features_test)
#save the output in CSV format
kaggle.kaggleize(predict, path+"Submission/Predictions/RobotArm/SupportVectorRegression_CrossValidated.csv")
kaggle.kaggleize(predict, path+"Submission/Predictions/RobotArm/best.csv")
#Plot graph representing Regression coefficient vs Error
plt.figure(1, figsize=(6,4))
plt.plot(errors1.keys(),errors1.values(),'sb-', linewidth=3) #Plot the first series in blue with square marker
plt.ylabel("Error") #Y-axis label
plt.xlabel("Regression Coefficient") #X-axis label
plt.title("Error vs Regression Coefficient for Robot Dataset") #Plot title
#Save the chart
plt.savefig(path+"/Submission/Figures/ErrorVsRegressionCoefficient_Robot.pdf")
plt.show()
#Plot graph representing Degree vs Error
plt.figure(2, figsize=(6,4))
plt.plot(errors2.keys(),errors1.values(),'or-', linewidth=3) #Plot the second series in red with square marker
plt.ylabel("Error") #Y-axis label
plt.xlabel("Degree") #X-axis label
plt.title("Degree vs Mean Square Error for Robot Dataset") #Plot title
#Save the chart
plt.savefig(path+"/Submission/Figures/ErrorVsDegree_Robot.pdf")
plt.show()
#model_selection.credit_card(train_x, train_y)
result_cc = model_selection.credit_card(train_x, train_y)
print(result_cc, "\n")
print("Best parameter is: ", min(result_cc, key = result_cc.get) )
clf = KernelRidge(alpha=0.0001, kernel='rbf', gamma=None)
clf.fit(train_x, train_y)
predicted_y = clf.predict(test_x)
# Output file location
file_name = '../Predictions/CreditCard/best.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name, True)
######################### 2.a
train_x, train_y, test_x = read_tumor_data()
print('Train=', train_x.shape)
print('Test=', test_x.shape)
result_t = model_selection.tumor(train_x, train_y)
print(result_t, "\n")
print("Best parameter is: ", max(result_t, key = result_t.get))
clf = SVC(C=1.0, kernel='rbf', gamma=0.001)
clf.fit(train_x, train_y)
predicted_y = clf.predict(test_x)
def kagglizing(predicted_y, best):
file_name = '../Predictions/' + best + '.csv'
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
def compute_DT(train_x, train_y, test_x):
# Different values of max_depth to run
depth = [3, 6, 9, 12, 15]
# Initialize Variables
train_score = [0] * len(depth)
test_score = [0] * len(depth)
fit_time = [0] * len(depth)
score_time = [0] * len(depth)
# make MAE scoring
MAE = make_scorer(compute_error, greater_is_better=False)
# indexing
index = 0
for d in depth:
# Create the model
regr = DecisionTreeRegressor(criterion="mae", max_depth=d)
# Cross Validation
cv_score = cross_validate(regr,
train_x,
train_y,
return_train_score=True,
scoring=MAE,
cv=5)
# Extract Statistics, scorer negates compute_error output
train_score[index] = -cv_score['train_score'].mean()
test_score[index] = -cv_score['test_score'].mean()
fit_time[index] = 1000 * cv_score['fit_time'].sum()
score_time[index] = 1000 * cv_score['score_time'].sum()
# Print Statistics
print('Depth of Decision Tree:', d)
print('train score', train_score[index])
print('test score', test_score[index])
print('fit time', fit_time[index])
print('score time', score_time[index])
print('===============================')
# Fit Model
regr.fit(train_x, train_y)
# Make Prediction
test_y = regr.predict(test_x)
# Create test output values
predicted_y = test_y * -1
# Output file location
file_name = '../Predictions/Decision_Tree_depth_%d.csv' % d
# Writing output in Kaggle format
print('Writing output to ', file_name)
kaggle.kaggleize(predicted_y, file_name)
# Increase indexing
index = index + 1
# Plot CV Time
plt.figure(num=None, figsize=(16, 8), dpi=80, facecolor='w', edgecolor='k')
plt.plot(depth, fit_time, '.')
plt.xlabel('Depth of Decision Tree')
plt.ylabel('Cross Validation Time [msec]')
plt.savefig('../Figures/DT_cv_time.png')
def choose_regression_model(problem_instance):
if problem_instance == 1:
#Load the Computer Activity Data
path = '../../Data/ComputerActivity/'
elif problem_instance == 2:
#Load the Housing Data
path = '../../Data/Housing/'
data = np.load(path + 'Data.npz')
features_train = data['X_train']
labels_train = data['y_train']
features_test = data['X_test']
labels_test = data['y_test']
n_estimator = []
print("Computer Activity:", features_train.shape, labels_train.shape, features_test.shape, labels_test.shape)
#Regression Method
if problem_instance == 1:
print("Executing Computer Activity problem")
#transform = feature_selection.SelectKBest(feature_selection.f_regression)
transform = feature_selection.RFECV(estimator = RidgeCV())
pipeline = Pipeline([('anova', transform), ('adr', ensemble.GradientBoostingRegressor(random_state=404))])
n_estimator = np.arange(75, 86, 1)
depth = range(6, 8)
#n_estimator = np.arange(10, 100, 10)
parameters = {'anova__cv': [5,10],
#'anova__k': np.arange(15, 22, 1),
'adr__n_estimators': n_estimator,
'adr__max_depth': depth
}
elif problem_instance == 2:
print("Executing Housing problem")
transform = feature_selection.SelectKBest(feature_selection.f_regression)
#transform = feature_selection.RFECV(estimator = RidgeCV())
n_estimator = np.arange(130, 160, 10)
#n_estimator = np.arange(10, 100, 1)
depth = range(6,8)
pipeline = Pipeline([('anova', transform), ('adr', ensemble.GradientBoostingRegressor(random_state=404))])
parameters = {'anova__k': np.arange(5, 9, 1),
#'anova__cv': [5,10],
'adr__n_estimators': n_estimator, #50
'adr__max_depth': depth
}
grid = grid_search.GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
grid.fit(features_train, labels_train)
predictions = grid.predict(features_test)
print(grid.best_params_, grid.best_score_, grid.best_estimator_, grid.grid_scores_)
scores = grid.grid_scores_
#print(type(scores), len(scores))
mean_score_list = []
parameters_list = []
for x in range(0, len(scores)):
mean_score_list.append(scores[x][1])
parameters_list.append(scores[x][0])
print(mean_score_list)
#print(parameters_list)
scores_list = np.array(mean_score_list)
plot_score_linechart(scores_list, problem_instance)
if problem_instance ==1:
kaggle.kaggleize(predictions, "../Predictions/ComputerActivity/test.csv")
else:
kaggle.kaggleize(predictions, "../Predictions/Housing/test.csv")